New MPC framework uses Gaussian Processes for robust control

Researchers have developed a new model predictive control (MPC) framework designed for uncertain nonlinear systems. This framework utilizes Gaussian Processes (GPs) to learn system dynamics from noisy measurements, incorporating robust predictions derived from contraction metrics. The proposed design ensures recursive feasibility, reliable constraint satisfaction, and convergence to a reference state with high probability, demonstrated through a numerical example involving a planar quadrotor. AI

IMPACT This research could lead to more reliable control systems in robotics and autonomous applications by improving how models adapt to uncertainty.

RANK_REASON The cluster contains an academic paper published on arXiv detailing a new control formulation. [lever_c_demoted from research: ic=1 ai=0.7]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gaussian process
• Gotit.pub
• Hugging Face
• Johannes Ignaz Kohler
• model predictive control
• ScienceCast

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